Bibliographic record
Abstract
See “Infliximab Dosing Strategies and Predicted Trough Exposure in Children With Crohn Disease” by Frymoyer et al on page 723. See “Practical Use of Infliximab Concentration Monitoring in Pediatric Crohn Disease” by Minar et al on page 715. Infliximab and other monoclonal antibodies targeting the cytokine, tumor necrosis factor alpha (TNFα), have greatly enhanced the treatment of inflammatory bowel disease (IBD) in children and in adults. Multicenter industry-led prospective pediatric clinical trials and observational studies involving young patients with Crohn disease (CD) and ulcerative colitis (UC) demonstrate that the majority of children achieve at least clinical “response” defined by disease-specific multi-item measures of disease activity. A proportion, however, will either never benefit (primary nonresponse) or will lose response over time (secondary loss of response). Moreover, some “responses” are partial and do not entail the optimal outcomes of steroid-free clinical remission and mucosal healing. Therapeutic drug monitoring (TDM) has emerged as an important tool for maximizing initial efficacy and subsequent durability of response (1). TDM (determination of levels of drug and antidrug antibodies) was introduced with infliximab therapy, initially as a means of assessing the basis for continuing symptoms (2). If drug levels measured at trough or at time of assessment of nonresponse are undetectable and antibody to infliximab (ATI) ideally negative or present in very low titre, dose escalation and/or interval shortening will likely benefit the patient with ongoing active inflammatory disease (2). Undetectable drug and high titre ATI, however, will likely require switching “within class” to another anti-TNF agent. Ongoing symptoms despite “adequate” drug levels, may have noninflammatory causes (eg, intestinal stricture or irritable bowel syndrome), but ongoing inflammatory disease in this situation mandates switching therapy “out of class” (2). Utilization of TDM to help assess unsatisfactory response, as exemplified in the single center report by Minar et al (3), is well established in adult and pediatric IBD practice, although clinical access to drug and antidrug antibody measurements remains variable in different jurisdictions worldwide. As the pharmacokinetics (what the body does to a drug) of infliximab have been defined; however, TDM has evolved beyond only assessment of poor response to be potentially a more important tool for up-front optimization of efficacy and durability of response over time (1,4). The simulation study reported by Frymoyer et al (5) challenges us to think in this way. Applying a published population-based pharmacokinetic model (6), the authors draw attention to the fact that one dosing regimen of infliximab does not fit all children with CD (5). Variability of drug clearance among patients suffering from the same chronic inflammatory disease is common to all monoclonal antibodies. Factors contributing to the observed pharmacokinetic heterogeneity include patient body size and sex, inflammatory burden (extent and severity of disease), serum albumin, and presence or absence of a concomitant immunomodulator and of anti-drug antibodies (1,7). These factors have recently been reviewed specifically with respect to acute severe UC, where inability to maintain infliximab in serum is particularly challenging (7). As reported on pages 723 to 727 of this issue of Journal of Pediatric Gastroenterology and Nutrition, these known predictors of trough levels were altered in a Monte Carlo simulation analysis to evaluate standard infliximab maintenance dosing regimens in children. Among typical (but simulated) pediatric CD patients, a target infliximab trough level of 3 ug/mL 8 weeks following the third induction dose (standard 5 mg/kg) was achieved in only 41% of patients with serum albumin of 4 g/L and in only 21% of those with hypoalbuminemia defined as serum albumin of 3 g/L (5). These data generated using the population-based pharmacokinetic model support the statement that “overall more aggressive dosing is predicted to be needed to consistently achieve a trough level of >3 μg/mL in children with CD” (5). Interestingly, while in the model both higher per kilogram dosing and shorter intervals increased the predicted trough concentration exposure, shortening the interval had a larger effect, an observation relevant to the question often asked concerning the best strategy for escalation of treatment. The selection of target trough levels in clinical practice has been guided by comparisons of rates of clinical remission across quartiles of trough concentrations in multicenter clinical trials (1,8). The target postinduction trough level of >3 μg/mL at week 14 in the simulation model is in keeping with the 3 to 7 μg/mL range beneficially utilized by Vande Casteele et al for patients already established on maintenance therapy in the optimization phase of the Trough level Adapted InfliXImab Treatment (TAXIT) trial (4). A target trough level needs to be sufficiently high to optimize efficacy, but the level, above which no additional benefit is derived, must also be identified. In the treatment of pediatric IBD, this “optimal therapeutic window” still needs refinement taking into account the clinical target (ie, symptom control versus mucosal healing). The published equation utilized in the model to calculate drug clearance, and thereby estimate concentration of drug remaining at trough, was derived from data concerning the 112 children in the REACH trial and 580 adults in the ACCENT I trial (6). One must question whether the validity of the simulation exercise in guiding treatment for young patients would have been stronger, had all data been generated in a larger, exclusively pediatric cohort. It behoves pediatric clinical investigators to design studies wherein levels of biologic agents are prospectively correlated with clinically important outcomes among real patients to further define the “optimal therapeutic window” and develop dosing regimens to target such concentrations of drug. In clinical practice, TDM should be used, ideally even via point-of-care testing, not just to assess loss of response, but preemptively to personalize biologic therapy with the goal of early optimization and maintenance of efficacy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".